A variety of technologies can be used to provide medical images. Typical modern sources of medical images are ultrasound scans, CT scans and MRI scans.
Most medical imaging involves images of humans. However, images may also be obtained of non-human animals, particularly as part of medical research projects. Medical images need to be read, analysed and reviewed by specialists.
Medical images may include information about a wide variety of anatomical features, structures, spaces and distances. For example, an image may show various types of healthy tissue, such as bone and/or organs, within the body. An image may also show abnormal tissues, such as tumours, cysts, swollen glands or other lesions. The word ‘tumour’ should henceforth be construed to include other types of abnormal tissues.
It is often necessary to estimate the size and volume of anatomical structures that are shown in medical images. The size of healthy tissue may be determined in order, for example, to measure growth in children.
These estimates may also serve, for example, to monitor the growth of abnormal tissue. Both the size of abnormal tissue, and the spacing between abnormal tissue and healthier tissue, may be of interest.
Here a ‘structure’ should be interpreted very broadly, and might include:
(i) A single entity, such as an organ, bone or tumour.
(ii) A group of essentially separate objects, such as ribs in a rib cage, or two separate tumours visible on one image.
(iii) A gap bordered by two or more objects or edges, such as the spacing between a tumour and the surface of a body, or between a tumour and a nearby organ.
Henceforth, a ‘structure’ that may be measured on an image may in fact be a single entity, or even a spacing between two different entities.
One prior art example where measurements are necessary is in the reading of images from cancer patients. Measurements of the principal dimensions of any suspected tumours are typically required for diagnosis and staging of disease. These measurements are also required for assessing the efficacy of any administered treatment.
In other fields of medicine, it may be necessary to obtain estimates of:
(i) The size or volume of normal anatomy, e.g. organs; or
(ii) Distances or angles between anatomical structures.
Much existing medical practice was developed for non-digital 2-d images, such as X-Ray films. Radiologists may obtain measurements directly from the hard copy of the image, using callipers or a ruler.
However, medical images are increasingly obtained and manipulated in digital format. In digital Radiology, either 2-d (‘2-d’) or 3-d (‘3-d’) images may be available. If the original image was only 2-d, then a user views a 2-d representation of that image.
A 3-d image from a scan typically includes a large volume of data points. Henceforth, the word ‘image’ describes the collection of all these data points. Normally therefore an ‘image’ will mean a 3-d image, but some embodiments of the present invention are applicable to 2-d images, such as those often obtained from ultrasound scans.
A user will normally view only one individual 2 dimensional ‘slice’ through the 3-d image. An image slice from a 3-d image is simply a 2-d representation, consisting of those data points that lie on a particular 2-d plane through the 3-d image. A typical 3-d image, such as one from an MRI scan, will have a matrix of regularly spaced data points. As a non-limiting example, the MRI-scan may have data points whose centres are spaced by 1 millimeter in the x- and y-directions across any plane of the scan. Consecutive planes may, for example, be parallel and separated by 7 millimeters.
Although it is usual for data points to be in the plane of a viewed image slice, it is possible that the points only lie near to the plane of the slice, for example when an ‘off-axis’ viewing angle has been selected.
Medical imaging workstations commonly provide tools for obtaining the required measurements directly from the displayed image slices.
Examples of the tools available on medical imaging workstations are digital callipers or a digital ruler. When working with such tools, the user is typically required to select and then click onto two reference points on the image slice. The workstation then calculates the real-world distance between the two reference points, and reports the result in cm or mm. Here ‘real-world’ means the distance on the object that was scanned, rather than the distance across the screen.
FIG. 1 generally illustrates a conventional ruler, available on current medical imaging workstations.
In FIG. 1, reference 100 indicates a medical image slice. Image slice 100 is displayed on a screen 105, which may be part of a medical imaging workstation.
Reference 110 indicates schematically the outer edge of a portion of medical image slice 100, i.e. of the object that was scanned. Reference 110 may, for example, indicate the outer edge of a torso.
Reference 112 shows a structure, also in cross-sectional view, that is located on medical image slice 100. Structure 112 might be a tumour, but might instead be an organ. Only portions of the tumour or organ that lie in the plane of the image slice 100 will be visible. Other portions that lie in neighbouring image slices, will not be visible on slice 100.
Reference 120 shows a linear ruler, which is unidirectional. A user has aligned linear ruler 120 between points 130 and 140 on structure 112, having selected these points as being of significance. The user has identified points 130 and 140 as being the end points of the longest axis or diameter of structure 112.
The linear ruler 120 then provides a readout of the distance between points 130 and 140. The readout is shown as 28 mm on FIG. 1. The numerical value of the measurement may be superimposed on the displayed slice 100, adjacent to the linear ruler 120, as shown in FIG. 1. Alternatively or in addition, the measurement may be displayed elsewhere on the screen, or recorded elsewhere, such as in a table accessible by viewing another screen.
Two examples of alternative workstation tools are:
(i) A virtual ruler. This tool requires the user to click, and sometimes also drag, either end of the ruler to the appropriate locations on the medical image.
(ii) A virtual protractor. This tool requires the user to click and drag either end of a pair of ‘hinged’ lines to the appropriate locations on the medical image. The workstation then reports the angular measurement.
Such tools as the virtual ruler and virtual protractor almost exclusively operate in 2D images, or in 2D slices extracted from 3D images.
The types of measurement of interest on a medical image may include:
(i) The largest diameter or length, in the plane of acquisition of the image. This diameter is known as the ‘long axis’.
(ii) The largest diameter or length, perpendicular to the long axis. This diameter is known as the ‘short axis’.
(iii) The volume.
(iv) One or more other distances.
(v) One or more angles.
Within the field of oncology, it is common clinical practice to measure the size of suspected tumours. This size measurement is made using the long and short axes, in a particular plane. Here ‘plane’ is important. It means a particular direction, in which the slice must be taken.
Two standard methods are in widespread use for the evaluation of treatment response in oncology. One, referred to as the ‘WHO’ standard, requires that both the long and short axes of each tumour be measured. The other method is the ‘RECIST’ standard. The ‘RECIST’ standard requires only measurement of the long axis of each tumour. In a recent version, RECIST 1.1, the measurement of the short axis is used for assessing lymph-nodes, instead of the measurement of the long axis.
The digital ruler and calliper tools provided on medical imaging workstations require the user to carry out either two or three manual steps. These steps are the following sequence, with at least step 1 and one of steps 2 or 3 being necessary:
Step 1: Manually select the slice, i.e. the particular 2-d image from amongst all the image data or images that were taken of the structure. Normally, the 2-d image slice that is selected will be the one that appears to have the longest tumour dimension.
Step 2: On screen, define the start and finish of the long axis measurement, and use a ruler or digital callipers to make the measurement.
Step 3: On screen, define the start and finish of the short axis measurement, and use a ruler or digital callipers to make the measurement.
The known prior art has a number of disadvantages regarding the accuracy, reproducibility and optimality of measurement tool control and placement. Some of these disadvantages are as follows:
(i) The user must judge the appropriate locations in the slice at which to specify the control points of the measurement tool, in order to obtain the required measurement. For some features, this is relatively straightforward. However, for other features, the true boundary may be unclear. The result is variability in the measurements obtained, for example by different users, even when those users are experienced.(ii) The interface for the interaction required, typically a computer mouse, may be hard to control precisely, leading to inaccurate placement or variability even between the same user making a measurement twice.(iii) The screen resolution at which the slice and interactive tool location are displayed may affect the accuracy to which the control point can be placed.(iv) For some types of measurement, such as the long axis and short axis, the approximate axis placement and direction may be easily discernable to the user. However, the selection of optimal image slice, axis direction and control point position placement may be significantly harder. This may lead to errors from arbitrary choice of placement. It may also lead to laborious measurements, when repeat measurements are undertaken for optimisation.(v) For measurements of the same object across a series of images, the measurements usually require consistency. This requirement may relate, for example, to slice orientation, slice choice, the definition of the object boundary, or axis direction. Although clinical users will attempt to be consistent, accurate and reproducible placement of control points to enforce such consistency across images in a series is difficult and error prone.
Increasingly, volumetric measurements are becoming of interest in medical imaging. Here it is necessary either to manually delineate the tumour or other structure, or to use automated or semi-automated algorithms. Examples of such algorithms are known from the following prior art publications:    (i) Marie-Pierre Jolly and Leo Grady, “3D General Lesion Segmentation in CT”, Proc. of ISBI 2008, Paris, France, May 14-17 2008. pp. 796-799.    (ii) Radiology reference: Zhao B, Reeves A P, Yankelevitz D, Henschke C I. Three-dimensional multi-criterion automatic segmentation of pulmonary nodules of helical CT images. Opt Eng 1999; 38:1340-1347.
Automated and semi-automated volumetric approaches attempt to overcome the limitations of ruler based approaches. They achieve this by enabling the user to define a 2-d or 3-d segmentation of the object requiring measurement, for example a tumour. The volume and maximum dimensions can then be derived from such segmentations.
Where a tumour has clear well-defined boundaries, such automated and semi-automated volumetric techniques can be useful. However, in many cases, medical images exhibit poorly defined boundaries. In addition, tumours are often within or adjacent to tissue of a similar radiological appearance. It is therefore difficult to distinguish tumour tissue from other tissues present in the same region of the image.
In such cases, existing tools for automated or semi-automated volumetric techniques either completely fail, or require a significant degree of user intervention to produce a successful result. This can be unsatisfactory, because in many cases the user is compelled to produce an accurate 2-d or 3-d segmentation, when all that was required was a simple 2-d linear measurement.
There is a need for a simple, fast, accurate and reproducible method for the placement of control points of measurement tools. This need arises when determining measurements of an object, such as healthy tissue, or a tumour or other structure, from a medical image.
Consistency of control point placement for measurements over a series of images is also important. This need applies to a series of images that are obtained by one technique at the same time, and to such a series obtained at different times. It also applies to multiple images obtained by different techniques, such as MRI and CT scans, whether during an investigation at one point in time or on different occasions.